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New SUN Programs framework unifies control and learning for robotic manipulation

By PulseAugur Editorial · [1 sources] ·

Researchers have developed SUN Programs, a novel framework that unifies model-based control and learned policies for complex manipulation tasks. This system, named Kuafu, automatically synthesizes these programs from language and scene semantics, enabling efficient training of policies. Kuafu demonstrated significant success rates on nine manipulation tasks, outperforming existing methods and achieving a higher successful trajectory time compared to human teleoperation. AI

IMPACT This research could lead to more robust and efficient robotic manipulation by better integrating symbolic planning with learned behaviors.

RANK_REASON The cluster contains an academic paper detailing a new research framework and system. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New SUN Programs framework unifies control and learning for robotic manipulation

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The cluster contains an academic paper detailing a new research framework and system. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Weiqi Wang, Zhi Li, Yudong Lei, David Martinez, Xiaofeng Gao, Yuxin Jiang, Chenfanfu Jiang, Yingnian Wu, Demetri Terzopoulos, Ran Gong ·

    SUN: Persistent Programs For Language-Grounded Control-to-Learning-to-Real Policies

    arXiv:2608.31167v1 Announce Type: cross Abstract: Bridging model-based control and learned policies in long-horizon manipulation has harbored a silent disagreement: control executes specified objectives, learning amortizes that behavior into a reactive policy, yet existing protoc…

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